Bibliographic record
Abstract
S’interroger sur le devenir de la gestion des ressources humaines suppose d’en saisir les tendances essentielles, d’en comprendre les évolutions, les ruptures et les continuités. C’est à cet exercice de « rétroprospective », qui combine précisément rétrospective et prospective, que s’essaient régulièrement les spécialistes de RH, explorant le passé pour éclairer l’avenir de la fonction ressource humaine. Parce qu’elle permet de saisir les dynamiques à l’œuvre, la démarche aide ainsi à se tenir à l’affût de changements émergents. Marcel Pochard a accepté pour Politique et Management Public d’effectuer ce « retour vers le futur ». Revenant sur le rapport du Conseil d’Etat de 2003, il mesure le chemin parcouru, s’interroge sur la persistance de difficultés et esquisse des futurs possibles pour la fonction publique.Marcel Pochard est conseiller d’État, ancien directeur général de l’administration et de la fonction publique (1993-1998). Il est également professeur associé à l’université de Paris I Panthéon-Sorbonne.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".